Explainable deep learning model to predict invasive bacterial infection in febrile young infants: A retrospective study-Reference-Cited by-同舟云学术

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Explainable deep learning model to predict invasive bacterial infection in febrile young infants: A retrospective study

Published:2023-04 Issue: Volume:172 Page:105007
ISSN:1386-5056
Container-title:International Journal of Medical Informatics
language:en
Short-container-title:International Journal of Medical Informatics

Author:

Yang Ying^ORCID,Wang Yi-Min,Lin Chun-Hung Richard,Cheng Chi-Yung^ORCID,Tsai Chi-Ming,Huang Ying-Hsien,Chen Tien-Yu,Chiu I-Min^ORCID

Publisher

Elsevier BV

Subject

Health Informatics

Reference38 articles.

1. Variation in care of the febrile young infant <90 days in US pediatric emergency departments;Aronson;Pediatrics,2014

2. Epidemiology of bacteremia in febrile infants in the United States;Biondi;Pediatrics,2013

3. Evaluation and Management of Well-Appearing Febrile Infants 8 to 60 Days Old;Pantell;Pediatrics,2021

4. Management of the Febrile Young Infant: Update for the 21st Century;Woll;Pediatr. Emerg. Care,2017

5. Predicting the outcome of neonatal bacterial meningitis;Klinger;Pediatrics,2000

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1. A comprehensive review of explainable AI for disease diagnosis;Array;2024-07

2. Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases;Machine Learning: Science and Technology;2024-05-30

3. Machine learning-based prediction of coronary care unit readmission: A multihospital validation study;DIGITAL HEALTH;2024-01

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